odibatting2007 <- read.csv("D:\\Vishal\\III year\\Data Analytics\\Assignment II\\Player Ratings\\2007odibattingrating.csv")
odibatting2008 <- read.csv("D:\\Vishal\\III year\\Data Analytics\\Assignment II\\Player Ratings\\2008odibattingrating.csv")
odibatting2009 <- read.csv("D:\\Vishal\\III year\\Data Analytics\\Assignment II\\Player Ratings\\2009odibattingrating.csv")
odibatting2010 <- read.csv("D:\\Vishal\\III year\\Data Analytics\\Assignment II\\Player Ratings\\2010odibattingrating.csv")
odibatting2011 <- read.csv("D:\\Vishal\\III year\\Data Analytics\\Assignment II\\Player Ratings\\2011odibattingrating.csv")
odibatting2012 <- read.csv("D:\\Vishal\\III year\\Data Analytics\\Assignment II\\Player Ratings\\2012odibattingrating.csv")
odibatting2013 <- read.csv("D:\\Vishal\\III year\\Data Analytics\\Assignment II\\Player Ratings\\2013odibattingrating.csv")
odibatting2014 <- read.csv("D:\\Vishal\\III year\\Data Analytics\\Assignment II\\Player Ratings\\2014odibattingrating.csv")
odibatting2015 <- read.csv("D:\\Vishal\\III year\\Data Analytics\\Assignment II\\Player Ratings\\2015odibattingrating.csv")
odibatting2016 <- read.csv("D:\\Vishal\\III year\\Data Analytics\\Assignment II\\Player Ratings\\2016odibattingrating.csv")
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
dataOdiBatting <- bind_rows(odibatting2007, odibatting2008, odibatting2009, odibatting2010,
odibatting2011, odibatting2012, odibatting2013, odibatting2014,
odibatting2015, odibatting2016)
## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector
summary(dataOdiBatting)
## Name Rating LogRating
## Length:1000 Min. :358.0 Min. :2.554
## Class :character 1st Qu.:452.0 1st Qu.:2.655
## Mode :character Median :518.5 Median :2.715
## Mean :539.6 Mean :2.723
## 3rd Qu.:617.0 3rd Qu.:2.790
## Max. :902.0 Max. :2.955
library(VIM)
## Loading required package: colorspace
## Loading required package: grid
## Loading required package: data.table
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
## VIM is ready to use.
## Since version 4.0.0 the GUI is in its own package VIMGUI.
##
## Please use the package to use the new (and old) GUI.
## Suggestions and bug-reports can be submitted at: https://github.com/alexkowa/VIM/issues
##
## Attaching package: 'VIM'
## The following object is masked from 'package:datasets':
##
## sleep
aggr(dataOdiBatting)
dataOdiBatting <- dataOdiBatting %>%
group_by(Name) %>%
summarise(avg = mean(Rating))
set.seed(20)
batcluster <- kmeans(dataOdiBatting[, 2], 5)
batcluster$cluster <- as.factor(batcluster$cluster)
str(batcluster)
## List of 9
## $ cluster : Factor w/ 5 levels "1","2","3","4",..: 1 5 3 5 2 5 3 2 5 4 ...
## $ centers : num [1:5, 1] 527 722 404 615 463
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:5] "1" "2" "3" "4" ...
## .. ..$ : chr "avg"
## $ totss : num 2410847
## $ withinss : num [1:5] 19309 29411 23091 24065 18282
## $ tot.withinss: num 114158
## $ betweenss : num 2296688
## $ size : int [1:5] 51 22 63 40 67
## $ iter : int 2
## $ ifault : int 0
## - attr(*, "class")= chr "kmeans"
library(ggplot2)
ggplot(dataOdiBatting, aes(dataOdiBatting$Name, avg, color = batcluster$cluster)) +
geom_point(size = 2) +
scale_color_hue(labels = c("Good", "Best", "Useless", "Better", "Average")) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
ggtitle("ODI Batting Ratings(2007-2016)")
dat <- arrange(dataOdiBatting, desc(avg)) %>%
mutate(rank = 1:nrow(dataOdiBatting))
dataOdiBatting <- merge(dataOdiBatting, dat, by = "Name")
dataOdiBatting
## Name avg.x avg.y rank
## 1 Kamran Akmal 521.0000 521.0000 93
## 2 Shoaib Malik 448.0000 448.0000 165
## 3 A A Mulla 369.0000 369.0000 239
## 4 A A Obanda 491.0000 491.0000 115
## 5 A B de Villiers 807.0000 807.0000 1
## 6 A Bagai 473.0000 473.0000 137
## 7 A Balbirnie 375.0000 375.0000 238
## 8 A C Gilchrist 723.0000 723.0000 10
## 9 A C Voges 482.0000 482.0000 122
## 10 A D Hales 612.0000 612.0000 43
## 11 A D Mathews 586.8571 586.8571 57
## 12 A D Russell 507.0000 507.0000 103
## 13 A Flintoff 550.5000 550.5000 70
## 14 A J Finch 625.0000 625.0000 37
## 15 A J Strauss 544.7500 544.7500 73
## 16 A M Rahane 520.6667 520.6667 94
## 17 A M Samad 420.3333 420.3333 197
## 18 A N Cook 548.7143 548.7143 71
## 19 A R Cusack 396.0000 396.0000 224
## 20 A Symonds 714.5000 714.5000 12
## 21 A T Rayudu 511.0000 511.0000 100
## 22 Abdul Razzaq 480.8000 480.8000 123
## 23 Aftab Ahmed 479.0000 479.0000 127
## 24 Ahmed Shehzad 554.7500 554.7500 68
## 25 Anamul Haque 470.2500 470.2500 141
## 26 Asad Shafiq 461.5000 461.5000 148
## 27 Asghar Stanikzai 421.0000 421.0000 196
## 28 Azhar Ali 491.0000 491.0000 116
## 29 B A Stokes 398.0000 398.0000 220
## 30 B B McCullum 616.5556 616.5556 40
## 31 B J Haddin 504.5714 504.5714 106
## 32 B J Hodge 450.0000 450.0000 162
## 33 B R M Taylor 572.1250 572.1250 62
## 34 B Zuiderent 401.0000 401.0000 216
## 35 Babar Azam 498.0000 498.0000 111
## 36 C A Ingram 447.5000 447.5000 166
## 37 C H Gayle 631.5000 631.5000 34
## 38 C J Anderson 562.3333 562.3333 65
## 39 C J Chibhabha 427.5000 427.5000 187
## 40 C J Ferguson 476.5000 476.5000 131
## 41 C K Coventry 429.0000 429.0000 185
## 42 C K Kapugedera 434.5000 434.5000 180
## 43 C Kieswetter 526.0000 526.0000 89
## 44 C L White 517.4000 517.4000 98
## 45 C O Obuya 448.6667 448.6667 164
## 46 C R Ervine 444.6667 444.6667 168
## 47 D A Miller 544.7500 544.7500 74
## 48 D A Warner 606.8000 606.8000 46
## 49 D J G Sammy 476.8571 476.8571 130
## 50 D J Hussey 480.0000 480.0000 125
## 51 D J J Bravo 472.8750 472.8750 138
## 52 D J Reekers 378.5000 378.5000 235
## 53 D L Hemp 377.0000 377.0000 237
## 54 D L Vettori 419.7500 419.7500 198
## 55 D M Bravo 531.0000 531.0000 85
## 56 D O Obuya 395.5000 395.5000 225
## 57 D P M Jayawardene 632.0000 632.0000 33
## 58 D R Smith 386.0000 386.0000 232
## 59 D Ramdin 429.4000 429.4000 184
## 60 D S Smith 415.0000 415.0000 203
## 61 D T Johnston 359.5000 359.5000 243
## 62 E C Joyce 476.5000 476.5000 132
## 63 E Chigumbura 496.3000 496.3000 113
## 64 E J G Morgan 608.3750 608.3750 45
## 65 E S Szwarczynski 413.0000 413.0000 206
## 66 F Behardien 457.5000 457.5000 156
## 67 F du Plessis 586.8000 586.8000 58
## 68 Fawad Alam 413.6667 413.6667 205
## 69 G B Hogg 379.0000 379.0000 234
## 70 G C Smith 686.1429 686.1429 20
## 71 G C Wilson 460.1667 460.1667 152
## 72 G D Elliott 475.7500 475.7500 135
## 73 G Gambhir 625.0000 625.0000 38
## 74 G J Bailey 655.2000 655.2000 24
## 75 G J Maxwell 603.2500 603.2500 48
## 76 G M Hamilton 470.0000 470.0000 142
## 77 G O Jones 426.5000 426.5000 189
## 78 G W Flower 414.5000 414.5000 204
## 79 H D R Thirimanne 520.2000 520.2000 95
## 80 H H Gibbs 696.0000 696.0000 15
## 81 H M Amla 789.0000 789.0000 2
## 82 H Masakadza 480.2000 480.2000 124
## 83 Haris Sohail 499.5000 499.5000 109
## 84 I H Romaine 361.0000 361.0000 242
## 85 I J L Trott 751.3333 751.3333 5
## 86 I K Pathan 421.3333 421.3333 195
## 87 I R Bell 599.3750 599.3750 50
## 88 I S Billcliff 398.0000 398.0000 221
## 89 Imran Farhat 423.6667 423.6667 191
## 90 Imran Nazir 391.0000 391.0000 231
## 91 Imrul Kayes 470.8333 470.8333 140
## 92 J C Buttler 641.3333 641.3333 31
## 93 J Charles 539.7500 539.7500 77
## 94 J D P Oram 508.3333 508.3333 102
## 95 J D Ryder 500.6667 500.6667 108
## 96 J E Root 634.2500 634.2500 32
## 97 J F Mooney 405.2000 405.2000 211
## 98 J H Kallis 672.8750 672.8750 22
## 99 J J Roy 534.0000 534.0000 82
## 100 J M Davison 412.0000 412.0000 208
## 101 J M How 522.0000 522.0000 92
## 102 J M Kemp 573.0000 573.0000 61
## 103 J Mubarak 403.0000 403.0000 215
## 104 J O Holder 405.0000 405.0000 212
## 105 J P Duminy 593.1111 593.1111 53
## 106 J P Faulkner 585.3333 585.3333 59
## 107 J R Hopes 469.0000 469.0000 144
## 108 J W A Taylor 447.0000 447.0000 167
## 109 Javed Ahmadi 378.0000 378.0000 236
## 110 Junaid Siddique 477.0000 477.0000 129
## 111 K A Pollard 522.6667 522.6667 91
## 112 K C Sangakkara 736.3750 736.3750 8
## 113 K J Coetzer 450.7500 450.7500 161
## 114 K J O'Brien 513.9000 513.9000 99
## 115 K K K K K K D Karthik 432.3333 432.3333 181
## 116 K O A Powell 405.0000 405.0000 213
## 117 K P Pietersen 649.4286 649.4286 26
## 118 K S Williamson 664.4000 664.4000 23
## 119 Kamran Akmal 497.8000 497.8000 112
## 120 Khurram Khan 416.0000 416.0000 200
## 121 L D Chandimal 547.2000 547.2000 72
## 122 L J Wright 416.0000 416.0000 201
## 123 L M P Simmons 544.0000 544.0000 75
## 124 L O B Cann 443.0000 443.0000 169
## 125 L P C Silva 519.2000 519.2000 97
## 126 L R Taylor 646.5000 646.5000 28
## 127 L Ronchi 526.5000 526.5000 88
## 128 L Vincent 530.0000 530.0000 87
## 129 M D K J Perera 476.3333 476.3333 133
## 130 M E K Hussey 742.0000 742.0000 6
## 131 M F Maharoof 393.3333 393.3333 229
## 132 M J Clarke 690.6250 690.6250 17
## 133 M J Guptill 631.3750 631.3750 35
## 134 M J Prior 442.5000 442.5000 171
## 135 M J Santner 405.0000 405.0000 214
## 136 M L Hayden 738.0000 738.0000 7
## 137 M N Samuels 492.2000 492.2000 114
## 138 M N Waller 431.6667 431.6667 183
## 139 M R Marsh 535.5000 535.5000 79
## 140 M S Dhoni 752.4000 752.4000 4
## 141 M S Sinclair 395.5000 395.5000 226
## 142 M S Wade 428.7500 428.7500 186
## 143 M T Thushara 394.0000 394.0000 227
## 144 M V Boucher 551.6000 551.6000 69
## 145 M W Machan 438.0000 438.0000 176
## 146 Mahmudullah 501.5714 501.5714 107
## 147 Mashrafe Mortaza 368.0000 368.0000 240
## 148 Misbah ul Haq 610.7143 610.7143 44
## 149 Moeen Ali 449.0000 449.0000 163
## 150 Mohammad Ashraful 479.2000 479.2000 126
## 151 Mohammad Hafeez 587.5000 587.5000 56
## 152 Mohammad Nabi 458.8000 458.8000 155
## 153 Mohammad Shahzad 441.0000 441.0000 174
## 154 Mohammad Yousuf 682.7500 682.7500 21
## 155 Mominul Haque 392.0000 392.0000 230
## 156 Mushfiqur Rahim 530.2500 530.2500 86
## 157 N Deonarine 438.0000 438.0000 177
## 158 N J O'Brien 460.1000 460.1000 153
## 159 N L McCullum 422.0000 422.0000 192
## 160 N L T Perera 396.5000 396.5000 223
## 161 Naeem Islam 415.5000 415.5000 202
## 162 Nasir Hossain 555.4000 555.4000 67
## 163 Nasir Jamshed 469.5000 469.5000 143
## 164 Noor Ali Zadran 421.5000 421.5000 193
## 165 Nowroz Mangal 441.0000 441.0000 175
## 166 O A Shah 562.0000 562.0000 66
## 167 P A Patel 417.0000 417.0000 199
## 168 P D Collingwood 644.5000 644.5000 30
## 169 P G Fulton 596.0000 596.0000 51
## 170 P J Hughes 506.5000 506.5000 104
## 171 P L Mommsen 401.0000 401.0000 217
## 172 P R Stirling 615.6667 615.6667 41
## 173 P W Borren 408.0000 408.0000 209
## 174 Q de Kock 692.6667 692.6667 16
## 175 R A Jadeja 488.5000 488.5000 118
## 176 R D Berrington 443.0000 443.0000 170
## 177 R Dravid 563.7500 563.7500 64
## 178 R G Sharma 563.7778 563.7778 63
## 179 R J Nicol 427.0000 427.0000 188
## 180 R N ten Doeschate 519.2500 519.2500 96
## 181 R R Rossouw 487.0000 487.0000 119
## 182 R R Sarwan 626.8333 626.8333 36
## 183 R R Watson 394.0000 394.0000 228
## 184 R S Bopara 476.3333 476.3333 134
## 185 R S Morton 498.3333 498.3333 110
## 186 R T Ponting 723.2000 723.2000 9
## 187 R V Uthappa 441.6667 441.6667 172
## 188 Raqibul Hasan 459.0000 459.0000 154
## 189 Rizwan Cheema 425.0000 425.0000 190
## 190 S B Styris 614.2500 614.2500 42
## 191 S C Ganguly 536.0000 536.0000 78
## 192 S C Williams 477.9000 477.9000 128
## 193 S Chanderpaul 715.7500 715.7500 11
## 194 S Chattergoon 363.0000 363.0000 241
## 195 S Dhaniram 412.3333 412.3333 207
## 196 S Dhawan 703.2500 703.2500 14
## 197 S E Marsh 484.0000 484.0000 121
## 198 S H T Kandamby 452.6667 452.6667 158
## 199 S K Raina 581.2000 581.2000 60
## 200 S M A Priyanjan 406.0000 406.0000 210
## 201 S M Pollock 540.0000 540.0000 76
## 202 S Matsikenyeri 452.8000 452.8000 157
## 203 S O Tikolo 451.2857 451.2857 160
## 204 S P D Smith 689.5000 689.5000 18
## 205 S R Tendulkar 707.0000 707.0000 13
## 206 S R Watson 616.8000 616.8000 39
## 207 S T Jayasuriya 647.0000 647.0000 27
## 208 Sabbir Rahman 436.5000 436.5000 178
## 209 Salman Butt 589.0000 589.0000 55
## 210 Samiullah Shenwari 504.6667 504.6667 105
## 211 Sarfraz Ahmed 460.5000 460.5000 151
## 212 Shahid Afridi 535.5000 535.5000 80
## 213 Shahriar Nafees 464.7500 464.7500 146
## 214 Shaiman Anwar 461.5000 461.5000 149
## 215 Shakib Al Hasan 594.1000 594.1000 52
## 216 Shamsur Rahman 398.0000 398.0000 222
## 217 Shoaib Malik 531.2222 531.2222 84
## 218 Sikandar Raza 490.5000 490.5000 117
## 219 Sohaib Maqsood 441.6667 441.6667 173
## 220 Soumya Sarkar 600.0000 600.0000 49
## 221 T D Paine 432.0000 432.0000 182
## 222 T L W Cooper 533.3333 533.3333 83
## 223 T M Dilshan 687.3000 687.3000 19
## 224 T M Odoyo 421.4286 421.4286 194
## 225 T Mishra 464.5000 464.5000 147
## 226 T T Bresnan 399.0000 399.0000 219
## 227 T T Samaraweera 485.0000 485.0000 120
## 228 T Taibu 469.0000 469.0000 145
## 229 T W M Latham 475.0000 475.0000 136
## 230 Tamim Iqbal 590.6667 590.6667 54
## 231 U T Khawaja 435.0000 435.0000 179
## 232 Umar Akmal 605.7143 605.7143 47
## 233 V Kohli 788.0000 788.0000 3
## 234 V Sehwag 646.0000 646.0000 29
## 235 V Sibanda 460.8750 460.8750 150
## 236 W Barresi 386.0000 386.0000 233
## 237 W T S Porterfield 471.4444 471.4444 139
## 238 W U Tharanga 509.7000 509.7000 101
## 239 W W Hinds 401.0000 401.0000 218
## 240 Y K Pathan 451.5000 451.5000 159
## 241 Yasir Hameed 535.0000 535.0000 81
## 242 Younis Khan 523.1111 523.1111 90
## 243 Yuvraj Singh 652.2857 652.2857 25
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
p <- plot_ly(dataOdiBatting, x = ~Name, y = ~avg.x, type = 'scatter',
mode = 'markers', color = batcluster$cluster,
text = ~paste('Rank: ', rank))
p
p <- plot_ly(dataOdiBatting, x = ~Name, y = ~avg.x, type = 'scatter', mode = 'markers', name = 'G1') %>%
add_trace(y = ~avg.x, name = 'Tree 2') %>%
add_trace(y = ~avg.x, name = 'Tree 3') %>%
add_trace(y = ~avg.x, name = 'Tree 4') %>%
add_trace(y = ~avg.x, name = 'Tree 5')
p
dat
## # A tibble: 243 x 3
## Name avg rank
## <chr> <dbl> <int>
## 1 A B de Villiers 807 1
## 2 H M Amla 789 2
## 3 V Kohli 788 3
## 4 M S Dhoni 752. 4
## 5 I J L Trott 751. 5
## 6 M E K Hussey 742 6
## 7 M L Hayden 738 7
## 8 K C Sangakkara 736. 8
## 9 R T Ponting 723. 9
## 10 A C Gilchrist 723 10
## # ... with 233 more rows
#Classification
library(party)
## Loading required package: mvtnorm
## Loading required package: modeltools
## Loading required package: stats4
## Loading required package: strucchange
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: sandwich
new_dat <- sample(2, nrow(dat), replace = TRUE, prob = c(0.7, 0.3))
train_data <- dat[new_dat == 1, ]
test_data <- dat[new_dat == 2, ]
myf <- avg~ rank
tree <- ctree(myf, data = train_data)
table(predict(tree), train_data$avg)
##
## 359.5 369 375 378.5 379 386 391 393.333333333333 394
## 376.142857142857 1 1 1 1 1 2 0 0 0
## 397.217948717949 0 0 0 0 0 0 1 1 2
## 422.862464985994 0 0 0 0 0 0 0 0 0
## 445.309693877551 0 0 0 0 0 0 0 0 0
## 463.836507936508 0 0 0 0 0 0 0 0 0
## 479.570346320346 0 0 0 0 0 0 0 0 0
## 492.514814814815 0 0 0 0 0 0 0 0 0
## 524.119692460317 0 0 0 0 0 0 0 0 0
## 553.58544973545 0 0 0 0 0 0 0 0 0
## 588.396472663139 0 0 0 0 0 0 0 0 0
## 613.148214285714 0 0 0 0 0 0 0 0 0
## 643.287261904762 0 0 0 0 0 0 0 0 0
## 719.349553571429 0 0 0 0 0 0 0 0 0
##
## 395.5 396 396.5 398 401 405 406 412 413 415 416 417
## 376.142857142857 0 0 0 0 0 0 0 0 0 0 0 0
## 397.217948717949 2 1 1 2 1 1 1 0 0 0 0 0
## 422.862464985994 0 0 0 0 0 0 0 1 1 1 1 1
## 445.309693877551 0 0 0 0 0 0 0 0 0 0 0 0
## 463.836507936508 0 0 0 0 0 0 0 0 0 0 0 0
## 479.570346320346 0 0 0 0 0 0 0 0 0 0 0 0
## 492.514814814815 0 0 0 0 0 0 0 0 0 0 0 0
## 524.119692460317 0 0 0 0 0 0 0 0 0 0 0 0
## 553.58544973545 0 0 0 0 0 0 0 0 0 0 0 0
## 588.396472663139 0 0 0 0 0 0 0 0 0 0 0 0
## 613.148214285714 0 0 0 0 0 0 0 0 0 0 0 0
## 643.287261904762 0 0 0 0 0 0 0 0 0 0 0 0
## 719.349553571429 0 0 0 0 0 0 0 0 0 0 0 0
##
## 419.75 421.333333333333 421.428571428571 421.5 422
## 376.142857142857 0 0 0 0 0
## 397.217948717949 0 0 0 0 0
## 422.862464985994 1 1 1 1 1
## 445.309693877551 0 0 0 0 0
## 463.836507936508 0 0 0 0 0
## 479.570346320346 0 0 0 0 0
## 492.514814814815 0 0 0 0 0
## 524.119692460317 0 0 0 0 0
## 553.58544973545 0 0 0 0 0
## 588.396472663139 0 0 0 0 0
## 613.148214285714 0 0 0 0 0
## 643.287261904762 0 0 0 0 0
## 719.349553571429 0 0 0 0 0
##
## 426.5 428.75 429 429.4 431.666666666667 432
## 376.142857142857 0 0 0 0 0 0
## 397.217948717949 0 0 0 0 0 0
## 422.862464985994 1 1 1 1 1 1
## 445.309693877551 0 0 0 0 0 0
## 463.836507936508 0 0 0 0 0 0
## 479.570346320346 0 0 0 0 0 0
## 492.514814814815 0 0 0 0 0 0
## 524.119692460317 0 0 0 0 0 0
## 553.58544973545 0 0 0 0 0 0
## 588.396472663139 0 0 0 0 0 0
## 613.148214285714 0 0 0 0 0 0
## 643.287261904762 0 0 0 0 0 0
## 719.349553571429 0 0 0 0 0 0
##
## 432.333333333333 435 436.5 441.666666666667 442.5 443
## 376.142857142857 0 0 0 0 0 0
## 397.217948717949 0 0 0 0 0 0
## 422.862464985994 1 0 0 0 0 0
## 445.309693877551 0 1 1 1 1 2
## 463.836507936508 0 0 0 0 0 0
## 479.570346320346 0 0 0 0 0 0
## 492.514814814815 0 0 0 0 0 0
## 524.119692460317 0 0 0 0 0 0
## 553.58544973545 0 0 0 0 0 0
## 588.396472663139 0 0 0 0 0 0
## 613.148214285714 0 0 0 0 0 0
## 643.287261904762 0 0 0 0 0 0
## 719.349553571429 0 0 0 0 0 0
##
## 444.666666666667 447 447.5 448.666666666667 450 450.75
## 376.142857142857 0 0 0 0 0 0
## 397.217948717949 0 0 0 0 0 0
## 422.862464985994 0 0 0 0 0 0
## 445.309693877551 1 1 1 1 1 1
## 463.836507936508 0 0 0 0 0 0
## 479.570346320346 0 0 0 0 0 0
## 492.514814814815 0 0 0 0 0 0
## 524.119692460317 0 0 0 0 0 0
## 553.58544973545 0 0 0 0 0 0
## 588.396472663139 0 0 0 0 0 0
## 613.148214285714 0 0 0 0 0 0
## 643.287261904762 0 0 0 0 0 0
## 719.349553571429 0 0 0 0 0 0
##
## 451.285714285714 452.8 457.5 459 460.1 460.166666666667
## 376.142857142857 0 0 0 0 0 0
## 397.217948717949 0 0 0 0 0 0
## 422.862464985994 0 0 0 0 0 0
## 445.309693877551 1 1 0 0 0 0
## 463.836507936508 0 0 1 1 1 1
## 479.570346320346 0 0 0 0 0 0
## 492.514814814815 0 0 0 0 0 0
## 524.119692460317 0 0 0 0 0 0
## 553.58544973545 0 0 0 0 0 0
## 588.396472663139 0 0 0 0 0 0
## 613.148214285714 0 0 0 0 0 0
## 643.287261904762 0 0 0 0 0 0
## 719.349553571429 0 0 0 0 0 0
##
## 460.5 460.875 461.5 464.5 464.75 469 470
## 376.142857142857 0 0 0 0 0 0 0
## 397.217948717949 0 0 0 0 0 0 0
## 422.862464985994 0 0 0 0 0 0 0
## 445.309693877551 0 0 0 0 0 0 0
## 463.836507936508 1 1 2 1 1 1 1
## 479.570346320346 0 0 0 0 0 0 0
## 492.514814814815 0 0 0 0 0 0 0
## 524.119692460317 0 0 0 0 0 0 0
## 553.58544973545 0 0 0 0 0 0 0
## 588.396472663139 0 0 0 0 0 0 0
## 613.148214285714 0 0 0 0 0 0 0
## 643.287261904762 0 0 0 0 0 0 0
## 719.349553571429 0 0 0 0 0 0 0
##
## 471.444444444444 472.875 475.75 476.333333333333
## 376.142857142857 0 0 0 0
## 397.217948717949 0 0 0 0
## 422.862464985994 0 0 0 0
## 445.309693877551 0 0 0 0
## 463.836507936508 1 1 0 0
## 479.570346320346 0 0 1 2
## 492.514814814815 0 0 0 0
## 524.119692460317 0 0 0 0
## 553.58544973545 0 0 0 0
## 588.396472663139 0 0 0 0
## 613.148214285714 0 0 0 0
## 643.287261904762 0 0 0 0
## 719.349553571429 0 0 0 0
##
## 476.857142857143 479 479.2 480 480.8 482 484 485 487
## 376.142857142857 0 0 0 0 0 0 0 0 0
## 397.217948717949 0 0 0 0 0 0 0 0 0
## 422.862464985994 0 0 0 0 0 0 0 0 0
## 445.309693877551 0 0 0 0 0 0 0 0 0
## 463.836507936508 0 0 0 0 0 0 0 0 0
## 479.570346320346 1 1 1 1 1 1 1 1 0
## 492.514814814815 0 0 0 0 0 0 0 0 1
## 524.119692460317 0 0 0 0 0 0 0 0 0
## 553.58544973545 0 0 0 0 0 0 0 0 0
## 588.396472663139 0 0 0 0 0 0 0 0 0
## 613.148214285714 0 0 0 0 0 0 0 0 0
## 643.287261904762 0 0 0 0 0 0 0 0 0
## 719.349553571429 0 0 0 0 0 0 0 0 0
##
## 488.5 490.5 491 492.2 496.3 497.8 498.333333333333
## 376.142857142857 0 0 0 0 0 0 0
## 397.217948717949 0 0 0 0 0 0 0
## 422.862464985994 0 0 0 0 0 0 0
## 445.309693877551 0 0 0 0 0 0 0
## 463.836507936508 0 0 0 0 0 0 0
## 479.570346320346 0 0 0 0 0 0 0
## 492.514814814815 1 1 2 1 1 1 1
## 524.119692460317 0 0 0 0 0 0 0
## 553.58544973545 0 0 0 0 0 0 0
## 588.396472663139 0 0 0 0 0 0 0
## 613.148214285714 0 0 0 0 0 0 0
## 643.287261904762 0 0 0 0 0 0 0
## 719.349553571429 0 0 0 0 0 0 0
##
## 501.571428571429 504.571428571429 509.7 511 517.4 520.2
## 376.142857142857 0 0 0 0 0 0
## 397.217948717949 0 0 0 0 0 0
## 422.862464985994 0 0 0 0 0 0
## 445.309693877551 0 0 0 0 0 0
## 463.836507936508 0 0 0 0 0 0
## 479.570346320346 0 0 0 0 0 0
## 492.514814814815 0 0 0 0 0 0
## 524.119692460317 1 1 1 1 1 1
## 553.58544973545 0 0 0 0 0 0
## 588.396472663139 0 0 0 0 0 0
## 613.148214285714 0 0 0 0 0 0
## 643.287261904762 0 0 0 0 0 0
## 719.349553571429 0 0 0 0 0 0
##
## 520.666666666667 530 530.25 531 531.222222222222
## 376.142857142857 0 0 0 0 0
## 397.217948717949 0 0 0 0 0
## 422.862464985994 0 0 0 0 0
## 445.309693877551 0 0 0 0 0
## 463.836507936508 0 0 0 0 0
## 479.570346320346 0 0 0 0 0
## 492.514814814815 0 0 0 0 0
## 524.119692460317 1 1 1 1 1
## 553.58544973545 0 0 0 0 0
## 588.396472663139 0 0 0 0 0
## 613.148214285714 0 0 0 0 0
## 643.287261904762 0 0 0 0 0
## 719.349553571429 0 0 0 0 0
##
## 533.333333333333 534 535 536 540 544 544.75 547.2
## 376.142857142857 0 0 0 0 0 0 0 0
## 397.217948717949 0 0 0 0 0 0 0 0
## 422.862464985994 0 0 0 0 0 0 0 0
## 445.309693877551 0 0 0 0 0 0 0 0
## 463.836507936508 0 0 0 0 0 0 0 0
## 479.570346320346 0 0 0 0 0 0 0 0
## 492.514814814815 0 0 0 0 0 0 0 0
## 524.119692460317 1 1 1 1 1 0 0 0
## 553.58544973545 0 0 0 0 0 1 2 1
## 588.396472663139 0 0 0 0 0 0 0 0
## 613.148214285714 0 0 0 0 0 0 0 0
## 643.287261904762 0 0 0 0 0 0 0 0
## 719.349553571429 0 0 0 0 0 0 0 0
##
## 548.714285714286 551.6 554.75 555.4 562
## 376.142857142857 0 0 0 0 0
## 397.217948717949 0 0 0 0 0
## 422.862464985994 0 0 0 0 0
## 445.309693877551 0 0 0 0 0
## 463.836507936508 0 0 0 0 0
## 479.570346320346 0 0 0 0 0
## 492.514814814815 0 0 0 0 0
## 524.119692460317 0 0 0 0 0
## 553.58544973545 1 1 1 1 1
## 588.396472663139 0 0 0 0 0
## 613.148214285714 0 0 0 0 0
## 643.287261904762 0 0 0 0 0
## 719.349553571429 0 0 0 0 0
##
## 562.333333333333 563.75 563.777777777778 573
## 376.142857142857 0 0 0 0
## 397.217948717949 0 0 0 0
## 422.862464985994 0 0 0 0
## 445.309693877551 0 0 0 0
## 463.836507936508 0 0 0 0
## 479.570346320346 0 0 0 0
## 492.514814814815 0 0 0 0
## 524.119692460317 0 0 0 0
## 553.58544973545 1 1 1 0
## 588.396472663139 0 0 0 1
## 613.148214285714 0 0 0 0
## 643.287261904762 0 0 0 0
## 719.349553571429 0 0 0 0
##
## 585.333333333333 586.857142857143 587.5 589
## 376.142857142857 0 0 0 0
## 397.217948717949 0 0 0 0
## 422.862464985994 0 0 0 0
## 445.309693877551 0 0 0 0
## 463.836507936508 0 0 0 0
## 479.570346320346 0 0 0 0
## 492.514814814815 0 0 0 0
## 524.119692460317 0 0 0 0
## 553.58544973545 0 0 0 0
## 588.396472663139 1 1 1 1
## 613.148214285714 0 0 0 0
## 643.287261904762 0 0 0 0
## 719.349553571429 0 0 0 0
##
## 590.666666666667 593.111111111111 594.1 596 599.375
## 376.142857142857 0 0 0 0 0
## 397.217948717949 0 0 0 0 0
## 422.862464985994 0 0 0 0 0
## 445.309693877551 0 0 0 0 0
## 463.836507936508 0 0 0 0 0
## 479.570346320346 0 0 0 0 0
## 492.514814814815 0 0 0 0 0
## 524.119692460317 0 0 0 0 0
## 553.58544973545 0 0 0 0 0
## 588.396472663139 1 1 1 1 0
## 613.148214285714 0 0 0 0 1
## 643.287261904762 0 0 0 0 0
## 719.349553571429 0 0 0 0 0
##
## 603.25 605.714285714286 606.8 608.375 610.714285714286
## 376.142857142857 0 0 0 0 0
## 397.217948717949 0 0 0 0 0
## 422.862464985994 0 0 0 0 0
## 445.309693877551 0 0 0 0 0
## 463.836507936508 0 0 0 0 0
## 479.570346320346 0 0 0 0 0
## 492.514814814815 0 0 0 0 0
## 524.119692460317 0 0 0 0 0
## 553.58544973545 0 0 0 0 0
## 588.396472663139 0 0 0 0 0
## 613.148214285714 1 1 1 1 1
## 643.287261904762 0 0 0 0 0
## 719.349553571429 0 0 0 0 0
##
## 614.25 615.666666666667 616.8 625 626.833333333333
## 376.142857142857 0 0 0 0 0
## 397.217948717949 0 0 0 0 0
## 422.862464985994 0 0 0 0 0
## 445.309693877551 0 0 0 0 0
## 463.836507936508 0 0 0 0 0
## 479.570346320346 0 0 0 0 0
## 492.514814814815 0 0 0 0 0
## 524.119692460317 0 0 0 0 0
## 553.58544973545 0 0 0 0 0
## 588.396472663139 0 0 0 0 0
## 613.148214285714 1 1 1 2 1
## 643.287261904762 0 0 0 0 0
## 719.349553571429 0 0 0 0 0
##
## 631.375 631.5 634.25 641.333333333333 644.5 646 647
## 376.142857142857 0 0 0 0 0 0 0
## 397.217948717949 0 0 0 0 0 0 0
## 422.862464985994 0 0 0 0 0 0 0
## 445.309693877551 0 0 0 0 0 0 0
## 463.836507936508 0 0 0 0 0 0 0
## 479.570346320346 0 0 0 0 0 0 0
## 492.514814814815 0 0 0 0 0 0 0
## 524.119692460317 0 0 0 0 0 0 0
## 553.58544973545 0 0 0 0 0 0 0
## 588.396472663139 0 0 0 0 0 0 0
## 613.148214285714 0 0 0 0 0 0 0
## 643.287261904762 1 1 1 1 1 1 1
## 719.349553571429 0 0 0 0 0 0 0
##
## 649.428571428571 652.285714285714 655.2 682.75
## 376.142857142857 0 0 0 0
## 397.217948717949 0 0 0 0
## 422.862464985994 0 0 0 0
## 445.309693877551 0 0 0 0
## 463.836507936508 0 0 0 0
## 479.570346320346 0 0 0 0
## 492.514814814815 0 0 0 0
## 524.119692460317 0 0 0 0
## 553.58544973545 0 0 0 0
## 588.396472663139 0 0 0 0
## 613.148214285714 0 0 0 0
## 643.287261904762 1 1 1 0
## 719.349553571429 0 0 0 1
##
## 686.142857142857 687.3 689.5 690.625 692.666666666667
## 376.142857142857 0 0 0 0 0
## 397.217948717949 0 0 0 0 0
## 422.862464985994 0 0 0 0 0
## 445.309693877551 0 0 0 0 0
## 463.836507936508 0 0 0 0 0
## 479.570346320346 0 0 0 0 0
## 492.514814814815 0 0 0 0 0
## 524.119692460317 0 0 0 0 0
## 553.58544973545 0 0 0 0 0
## 588.396472663139 0 0 0 0 0
## 613.148214285714 0 0 0 0 0
## 643.287261904762 0 0 0 0 0
## 719.349553571429 1 1 1 1 1
##
## 703.25 707 714.5 715.75 723 736.375 751.333333333333
## 376.142857142857 0 0 0 0 0 0 0
## 397.217948717949 0 0 0 0 0 0 0
## 422.862464985994 0 0 0 0 0 0 0
## 445.309693877551 0 0 0 0 0 0 0
## 463.836507936508 0 0 0 0 0 0 0
## 479.570346320346 0 0 0 0 0 0 0
## 492.514814814815 0 0 0 0 0 0 0
## 524.119692460317 0 0 0 0 0 0 0
## 553.58544973545 0 0 0 0 0 0 0
## 588.396472663139 0 0 0 0 0 0 0
## 613.148214285714 0 0 0 0 0 0 0
## 643.287261904762 0 0 0 0 0 0 0
## 719.349553571429 1 1 1 1 1 1 1
##
## 752.4 788 789
## 376.142857142857 0 0 0
## 397.217948717949 0 0 0
## 422.862464985994 0 0 0
## 445.309693877551 0 0 0
## 463.836507936508 0 0 0
## 479.570346320346 0 0 0
## 492.514814814815 0 0 0
## 524.119692460317 0 0 0
## 553.58544973545 0 0 0
## 588.396472663139 0 0 0
## 613.148214285714 0 0 0
## 643.287261904762 0 0 0
## 719.349553571429 1 1 1
plot(tree)
test_tree <- ctree(myf, data = test_data)
plot(test_tree)
